computer-vision-9

Semantic correspondence

3260 papers • 126 benchmarks • 313 datasets

The task of semantic correspondence aims to establish reliable visual correspondence between different instances of the same object category.

(Image credit: Papersgraph)

Benchmarks

These leaderboards are used to track progress in semantic-correspondence-9

Trend

Dataset

Best Model

Actions

SPair-71k

PF-PASCAL

PF-WILLOW

Libraries

i

Use these libraries to find semantic-correspondence-9 models and implementations

Datasets

Subtasks

Interspecies Facial Keypoint Transfer

Most implemented papers

Neighbourhood Consensus Networks

T. Pajdla, Josef Sivic, Mircea Cimpoi, A. Torii, Relja Arandjelović, Ignacio Rocco•Tue Oct 23 2018

An end-to-end trainable convolutional neural network architecture that identifies sets of spatially consistent matches by analyzing neighbourhood consensus patterns in the 4D space of all possible correspondences between a pair of images without the need for a global geometric model is developed.

430

Content

Caltech-101

CUB-200-2011

AVMIT

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PatentMatch: A Dataset for Matching Patent Claims & Prior Art

Christoph Hewel, Julian Risch, Nicolas Alder, Ralf Krestel•Sat Dec 26 2020

A training dataset for supervised machine learning called PatentMatch, which contains pairs of claims from patent applications and semantically corresponding text passages of different degrees from cited patent documents, is created to address the computer-assisted search for prior art.

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DiscoBox: Weakly Supervised Instance Segmentation and Semantic Correspondence from Box Supervision

Anima Anandkumar, Yuke Zhu, Zhiding Yu, Guilin Liu, Larry S. Davis, C. Choy, Shiyi Lan, Subhashree Radhakrishnan•Wed May 12 2021

A self-ensembling framework where instance segmentation and semantic correspondence are jointly guided by a structured teacher in addition to the bounding box supervision, which shows a symbiotic relationship where the two tasks mutually benefit from each other.

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Color2Embed: Fast Exemplar-Based Image Colorization using Color Embeddings

Wenhao Wu, Yihao Liu, Dongliang He, Hengyuan Zhao•Mon Jun 14 2021

This paper presents a fast exemplar-based image colorization approach using color embeddings named Color2Embed, which adopts a self-augmented self-reference learning scheme, where the reference image is generated by graphical transformations from the original colorful one whereby the training can be formulated in a paired manner.

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End-to-End Weakly-Supervised Semantic Alignment

Josef Sivic, Relja Arandjelović, Ignacio Rocco•Mon Dec 18 2017

A convolutional neural network architecture for semantic alignment that is trainable in an end-to-end manner from weak image-level supervision in the form of matching image pairs that computes the quality of the alignment based on only geometrically consistent correspondences thereby reducing the effect of background clutter.

187 0

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iSPA-Net: Iterative Semantic Pose Alignment Network

R. Venkatesh Babu, Jogendra Nath Kundu, Aditya Ganeshan, V. RahulM., Aditya Prakash•Thu Aug 02 2018

This work proposes an iterative Semantic Pose Alignment Network, called iSPA-Net, which focuses on exploiting semantic 3D structural regularity to solve the task of fine-grained pose estimation by predicting viewpoint difference between a given pair of images.

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SnapMix: Semantically Proportional Mixing for Augmenting Fine-grained Data

D. Tao, Xinchao Wang, Shaoli Huang•Tue Dec 08 2020

Semantically Proportional Mixing (SnapMix) is proposed that exploits class activation map (CAM) to lessen the label noise in augmenting fine-grained data and consistently outperforms existing mixed-based approaches regardless of different datasets or network depths.

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Cost Aggregation Is All You Need for Few-Shot Segmentation

Seungryong Kim, Sunghwan Hong, Seokju Cho, Jisu Nam•Tue Dec 21 2021

This work introduces a novel cost aggregation network, dubbed Volumetric Aggregation with Transformers (VAT), to tackle the few-shot segmentation task by using both convolutions and transformers to efficiently handle high dimensional correlation maps between query and support.

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Going Denser with Open-Vocabulary Part Segmentation

Saining Xie, Zhicheng Yan, Ping Luo, Pei Sun, Chenchen Zhu, Shoufa Chen, Fanyi Xiao•Wed May 17 2023

A detector with the ability to predict both open-vocabulary objects and their part segmentation and a detector that generalizes to a wide range of part segmentation datasets while achieving better performance than dataset-specific training is proposed.

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Capturing Semantic Similarity for Entity Linking with Convolutional Neural Networks

D. Klein, Greg Durrett, Matthew Francis-Landau•Thu Mar 31 2016

This work presents a model that uses convolutional neural networks to capture semantic correspondence between a mention's context and a proposed target entity, outperforming the prior systems of Durrett and Klein (2014) and Nguyen et al. (2014).

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